Clinical AI Validation Methods
This research note summarizes practical validation approaches for clinical AI: retrospective benchmarking, prospective pilots, bias assessment, and ongoing post-deployment monitoring.
Key approaches
- Retrospective benchmarking: Evaluate performance on held-out datasets and across subpopulations.
- Prospective shadow deployments: Run the model in parallel with standard care to measure concordance and operational impact without affecting decisions.
- Bias & fairness audits: Compare performance across demographic groups and examine calibration.
- Continuous monitoring: Post-deployment tracking of drift, outcomes, and adverse events.